Associate Professor in Machine Learning
Oct 2025 - PresentUAX University
AI & Computation DegreeBuilt the Machine Learning curriculum from scratch for the undergraduate AI & Computation program — course design, labs, projects, and assessments.
Courses
Machine Learning I — Foundations
- Supervised learning: regression and classification (linear, polynomial, logistic, trees, k-NN, SVM)
- Model evaluation: train/test split, cross-validation, confusion matrices, precision, recall, F1, ROC
- Hands-on Python labs with Scikit-Learn, NumPy, and Pandas
RegressionClassificationModel EvaluationCross-Validation
Machine Learning II — Advanced Topics
- Unsupervised learning: K-means, hierarchical clustering, DBSCAN
- Dimensionality reduction: PCA and t-SNE
- Ensembles and tuning: Random Forest, XGBoost, grid/random/Bayesian search
- End-to-end capstone projects on real-world datasets
ClusteringEnsemble MethodsHyperparameter TuningDimensionality Reduction
Approach
- Theory first, then live coding sessions where students implement from scratch
- Real-world datasets and integrated projects that combine multiple concepts
- Continuous feedback through labs, assessments, and project reviews
At a glance
20+ StudentsFull Curriculum DesignPython + Scikit-LearnTheory + PracticeReal-World ProjectsHands-On Labs